Yankun Peng

MODELING MULTISPECTRAL SCATTERING PROFILES FOR PREDICTION OF APPLE FRUIT FIRMNESS

Nondestructive measurement of fruit firmness would allow the fruit industry to deliver superior, consistent fruit to the marketplace and ensure consumer acceptance and satisfaction.The objective of this research was todevelop mathematical models to describe the relationship between fruit firmness and multispectral scattering profiles from apples. Scattering images were acquired from Red Delicious apples using two different multispectral imaging systems at wavelengths 680 nm, 880 nm, 905 nm, and 940 nm with a bandpass of 10 nm. Radial scattering profiles were described accurately by a Lorentzian distribution function with three independent profile parameters. Firmness prediction models were constructed using multi- linear regression against twelve Lorentzian parameters for four wavelengths, and they were verified with separate sets of apple fruit. The prediction models gave firmness predictions with the correlation coefficient (r) of 0.82 and the standard error for validation (SEV) of 6.39 N for one set of apple samples, and r = 0.76 and SEV = 6.01 N for another set.

Nondestructive assessment of beef-marbling grade using hyperspectral imaging technology

The objective of this study was to assess the beef-marbling grade using hyperspectral imaging technology. A hyperspectral scanning imaging system was developed to collect hyperspectral images in the spectral region of 400–1100 nm. Original turned line-scanning hyperspectral images were transformed into three-dimensional spectral data with a computer program developed by authors. The maximal ratio of gray value of fat and lean in each band was used to select characteristic bands. As a result, the images at 530nm were used to differentiate beef-marbling. Three characteristic parameters were extracted, and used to establish prediction model by multiple linear regression (MLR) methods. The MLR model gave a good result with R2 = 0.92, SECV = 0.45. The research demonstrated that hyperspectral imaging technology is useful for nondestructive determination of beef marbling level.

Raman spectroscopy and imaging to detect contaminants for food safety applications

This study presents the use of Raman chemical imaging for the screening of dry milk powder for the presence of chemical contaminants and Raman spectroscopy for quantitative assessment of chemical contaminants in liquid milk. For image-based screening, melamine was mixed into dry milk at concentrations (w/w) between 0.2% and 10.0%, and images of the mixtures were analyzed by a spectral information divergence algorithm. Ammonium sulfate, dicyandiamide, and urea were each separately mixed into dry milk at concentrations (w/w) between 0.5% and 5.0%, and an algorithm based on self-modeling mixture analysis was applied to these sample images. The contaminants were successfully detected and the spatial distribution of the contaminants within the sample mixtures was visualized using these algorithms. Liquid milk mixtures were prepared with melamine at concentrations between 0.04% and 0.30%, with ammonium sulfate and with urea at concentrations between 0.1% and 10.0%, and with dicyandiamide at concentrations between 0.1% and 4.0%. Analysis of the Raman spectra from the liquid mixtures showed linear relationships between the Raman intensities and the chemical concentrations. Although further studies are necessary, Raman chemical imaging and spectroscopy show promise for use in detecting and evaluating contaminants in food ingredients.

Measurement of the Optical Properties of Apples by Hyperspectral Imaging for Assessing Fruit Quality

Absorption and reduced scattering coefficients are two fundamental optical properties for turbid materials. The objective of this research was to use a newly developed hyperspectral imaging technique for determining the absorption and reduced scattering coefficients of apples and to relate them to fruit firmness and soluble solids content. Hyperspectral images were acquired from 650 ‘Golden Delicious’ apples. The absorption and reduced scattering coefficients of the apples were determined using an inverse algorithm to fit the diffusion theory model to individual scattering profiles over the wavelengths of 530-950 nm. Values of the absorption and reduced scattering coefficients were in the range of 0.0-0.3 cm-1 and 11- 19 cm-1, respectively. For most apples, there was a predominant peak around 675 nm due to chlorophyll absorption. Spectra of the reduced scattering coefficient decreased monotonically with the increasing wavelength. Both absorption and reduced scattering coefficients were correlated with fruit firmness, with the correlation coefficient of 0.56-0.66. Poor correlation between fruit soluble solids content and the absorption coefficient was obtained, which could be due to relatively high fitting errors for the absorption coefficient. Improvements in hardware and computational algorithm are needed for more accurate, reliable measurements of the optical properties of food and agricultural products.

Nondestructive evaluation of soluble solid content in strawberry by near infrared spectroscopy

This paper indicates the feasibility to use near infrared (NIR) spectroscopy combined with synergy interval partial least squares (siPLS) algorithms as a rapid nondestructive method to estimate the soluble solid content (SSC) in strawberry. Spectral preprocessing methods were optimized selected by cross-validation in the model calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The performance of the final model was back-evaluated according to root mean square error of calibration (RMSEC) and correlation coefficient (R2 c) in calibration set, and tested by mean square error of prediction (RMSEP) and correlation coefficient (R2 p) in prediction set. The optimal siPLS model was obtained with after first derivation spectra preprocessing. The measurement results of best model were achieved as follow: RMSEC = 0.2259, R2 c = 0.9590 in the calibration set; and RMSEP = 0.2892, R2 p = 0.9390 in the prediction set. This work demonstrated that NIR spectroscopy and siPLS with efficient spectral preprocessing is a useful tool for nondestructively evaluation SSC in strawberry.

A Spatially Offset Raman Spectroscopy Method for Non-Destructive Detection of Gelatin-Encapsulated Powders

Non-destructive subsurface detection of encapsulated, coated, or seal-packaged foods and pharmaceuticals can help prevent distribution and consumption of counterfeit or hazardous products. This study used a Spatially Offset Raman Spectroscopy (SORS) method to detect and identify urea, ibuprofen, and acetaminophen powders contained within one or more (up to eight) layers of gelatin capsules to demonstrate subsurface chemical detection and identification. A 785-nm point-scan Raman spectroscopy system was used to acquire spatially offset Raman spectra for an offset range of 0 to 10 mm from the surfaces of 24 encapsulated samples, using a step size of 0.1 mm to obtain 101 spectral measurements per sample. As the offset distance was increased, the spectral contribution from the subsurface powder gradually outweighed that of the surface capsule layers, allowing for detection of the encapsulated powders. Containing mixed contributions from the powder and capsule, the SORS spectra for each sample were resolved into pure component spectra using self-modeling mixture analysis (SMA) and the corresponding components were identified using spectral information divergence values. As demonstrated here for detecting chemicals contained inside thick capsule layers, this SORS measurement technique coupled with SMA has the potential to be a reliable non-destructive method for subsurface inspection and authentication of foods, health supplements, and pharmaceutical products that are prepared or packaged with semi-transparent materials.

Classification of Pork Quality Characteristics by Hyperspectral Scattering Technique

Meat quality and safety losses may occur as a consequence of microbiological, enzymatic and chemical changes during meat storage. In this study, a hyperspectral imaging technique was developed to achieve fast, nondestructive and objective determination of the microbial spoilage of pork based on the indicator of total viable counts (TVC). Fresh pork was purchased from a local supermarket and stored at 4°C for 1-15 days. Totally 34 samples were used in the experiment and 2-4 samples were taken out randomly each day for collecting hyperspectral images and reference microbiological tests. [*abstract continues following Introduction...]

Rapid detection of benzoyl peroxide in wheat flour by using Raman scattering spectroscopy

Benzoyl peroxide is a common flour additive that improves the whiteness of flour and the storage properties of flour products. However, benzoyl peroxide adversely affects the nutritional content of flour, and excess consumption causes nausea, dizziness, other poisoning, and serious liver damage. This study was focus on detection of the benzoyl peroxide added in wheat flour. A Raman scattering spectroscopy system was used to acquire spectral signal from sample data and identify benzoyl peroxide based on Raman spectral peak position. The optical devices consisted of Raman spectrometer and CCD camera, 785 nm laser module, optical fiber, prober, and a translation stage to develop a real-time, nondestructive detection system. Pure flour, pure benzoyl peroxide and different concentrations of benzoyl peroxide mixed with flour were prepared as three sets samples to measure the Raman spectrum. These samples were placed in the same type of petri dish to maintain a fixed distance between the Raman CCD and petri dish during spectral collection. The mixed samples were worked by pretreatment of homogenization and collected multiple sets of data of each mixture. The exposure time of this experiment was set at 0.5s. The Savitzky Golay (S-G) algorithm and polynomial curve-fitting method was applied to remove the fluorescence background from the Raman spectrum. The Raman spectral peaks at 619 cm-1, 848 cm-1, 890 cm-1, 1001 cm-1, 1234 cm-1, 1603cm-1, 1777cm-1 were identified as the Raman fingerprint of benzoyl peroxide. Based on the relationship between the Raman intensity of the most prominent peak at around 1001 cm-1 and log values of benzoyl peroxide concentrations, the chemical concentration prediction model was developed. This research demonstrated that Raman detection system could effectively and rapidly identify benzoyl peroxide adulteration in wheat flour. The experimental result is promising and the system with further modification can be applicable for more products in near future.

Improving prediction of total viable counts in pork based on hyperspectral scattering technique

A hyperspectral scattering technique was investigated for predicting the total viable counts (TVC) of pork in the article. Fresh pork was purchased from a local market and stored at 4°C for 1-15 days. Totally 35 samples were used in the experiment and 2-4 samples were taken out randomly each day for collecting hyperspectral images and reference microbiological tests. Gompertz function was applied to fit the scattering profiles of pork and Teflon, and the fitting results were pretty good in the spectral range of 470-1010 nm. Both individual parameters and integrated parameters were explored to develop the multi-linear regression models for predicting pork TVC, and the results indicated that individual Gompertz parameter α was superior to other individual parameters, while the integrated parameters can perform better. The best result for predicting pork TVC was achieved by the form of (α, β, ε), with the RCV of 0.963. The study demonstrated that hyperspectral scattering technique combined with Gompertz function was potential for rapid determination of pork TVC, and would be a valid tool for monitoring the quality and safety attributes of meat in the future.

A Method for Determining Organophosphorus Pesticide Concentration Based on Near-Infrared Spectroscopy

The traditional methods for determining pesticide concentrations are time-consuming, complicated, and require extensive pretreatment processes. In this study, near-infrared (NIR) spectroscopy was used to determine trace chemicals. The dry-extract system for infrared (DESIR) technique was used to prepare samples. Filter paper was used as the substrate. Pesticide solutions were prepared by dissolving a commercial pesticide in distilled water at different concentrations (1.25 to 400 mg kg-1). Samples were prepared by pipetting the solution onto the filter paper and then evaporating it in a vacuum drying oven. Spectral curves of the samples were acquired in the range of 10000 to 4000 cm-1 using an NIR spectrometer. Partial least squares regression (PLSR) was used to establish prediction models. The best prediction result was obtained using PLSR with multiplicative scatter correction (MSC) and first derivation as the pretreatment procedure. The process was able to predict the concentrations of chlorpyrifos with R = 0.899. A support vector machine (SVM) was used to establish a classification model. The result showed that 89.286% of samples were correctly predicted when the sample set was divided into three classes of chlorpyrifos content ( 300 mg kg-1).